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Sensor Integration Using State Estimators

  • J. G. Balchen
  • F. Dessen
  • G. Skofteland
Part of the NATO ASI Series book series (volume 63)

Abstract

Means for including very different types of sensors using one single unit are described. Accumulated data are represented using an undatable dynamic model, a Kaiman filter. The scheme easily handles common phenomena such as skewed sampling, finite resolution measurements and information delays. Included is an example where 3D motion information is collected by one or more vision sensors.

Keywords

Kalman Filter Extend Kalman Filter Measurement Vector Sensor Integration Autonomous Vehicle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Broida, T.J., Chellappa, R. (1986): Estimation of Object Motion Parameters from Noisy Images. IEEE Trans. on Pattern Analysis and Machine Intelligence, PAMI-8(1):90–98CrossRefGoogle Scholar
  2. Dickmanns, E. D., Zapp, A. (1985): Guiding Land Vehicles along Roadways by Computer Vision. Proc. Congres Automatique 1985, The Tools for Tomorrow, Toulouse, 233–244Google Scholar
  3. Jazwinski, A. H. (1970): Stochastic Processes and Filtering Theory. Academic Press, New YorkMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1990

Authors and Affiliations

  • J. G. Balchen
    • 1
  • F. Dessen
    • 1
  • G. Skofteland
    • 1
  1. 1.Division of Engineering CyberneticsNorwegian Institute of TechnologyTrondheimNorway

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